Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations90615
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Diameter is highly overall correlated with Height and 6 other fieldsHigh correlation
Height is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Length is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Rings is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Shell weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Whole weight is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Whole weight.1 is highly overall correlated with Diameter and 6 other fieldsHigh correlation
Whole weight.2 is highly overall correlated with Diameter and 6 other fieldsHigh correlation
id is uniformly distributed Uniform
id has unique values Unique

Reproduction

Analysis started2025-05-18 18:13:33.496160
Analysis finished2025-05-18 18:13:39.088853
Duration5.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct90615
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45307
Minimum0
Maximum90614
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:39.129840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4530.7
Q122653.5
median45307
Q367960.5
95-th percentile86083.3
Maximum90614
Range90614
Interquartile range (IQR)45307

Descriptive statistics

Standard deviation26158.442
Coefficient of variation (CV)0.57735983
Kurtosis-1.2
Mean45307
Median Absolute Deviation (MAD)22654
Skewness0
Sum4.1054938 × 109
Variance6.8426407 × 108
MonotonicityStrictly increasing
2025-05-18T15:13:39.203080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90614 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
90598 1
 
< 0.1%
90597 1
 
< 0.1%
90596 1
 
< 0.1%
Other values (90605) 90605
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
90614 1
< 0.1%
90613 1
< 0.1%
90612 1
< 0.1%
90611 1
< 0.1%
90610 1
< 0.1%
90609 1
< 0.1%
90608 1
< 0.1%
90607 1
< 0.1%
90606 1
< 0.1%
90605 1
< 0.1%

Sex
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size708.1 KiB
I
33093 
M
31027 
F
26495 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters90615
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowI
4th rowM
5th rowI

Common Values

ValueCountFrequency (%)
I 33093
36.5%
M 31027
34.2%
F 26495
29.2%

Length

2025-05-18T15:13:39.261688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-18T15:13:39.297439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
i 33093
36.5%
m 31027
34.2%
f 26495
29.2%

Most occurring characters

ValueCountFrequency (%)
I 33093
36.5%
M 31027
34.2%
F 26495
29.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 90615
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 33093
36.5%
M 31027
34.2%
F 26495
29.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 90615
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 33093
36.5%
M 31027
34.2%
F 26495
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 33093
36.5%
M 31027
34.2%
F 26495
29.2%

Length
Real number (ℝ)

High correlation 

Distinct157
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51709842
Minimum0.075
Maximum0.815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:39.350864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.075
5-th percentile0.28
Q10.445
median0.545
Q30.6
95-th percentile0.68
Maximum0.815
Range0.74
Interquartile range (IQR)0.155

Descriptive statistics

Standard deviation0.11821671
Coefficient of variation (CV)0.22861549
Kurtosis0.1333638
Mean0.51709842
Median Absolute Deviation (MAD)0.07
Skewness-0.73201519
Sum46856.874
Variance0.01397519
MonotonicityNot monotonic
2025-05-18T15:13:39.427044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.575 3267
 
3.6%
0.58 2670
 
2.9%
0.57 2167
 
2.4%
0.55 2122
 
2.3%
0.595 1992
 
2.2%
0.525 1985
 
2.2%
0.6 1961
 
2.2%
0.585 1911
 
2.1%
0.53 1908
 
2.1%
0.565 1906
 
2.1%
Other values (147) 68726
75.8%
ValueCountFrequency (%)
0.075 4
 
< 0.1%
0.09 3
 
< 0.1%
0.1 2
 
< 0.1%
0.105 1
 
< 0.1%
0.11 11
< 0.1%
0.115 1
 
< 0.1%
0.12 2
 
< 0.1%
0.125 2
 
< 0.1%
0.13 24
< 0.1%
0.135 16
< 0.1%
ValueCountFrequency (%)
0.815 3
 
< 0.1%
0.8 5
 
< 0.1%
0.78 12
 
< 0.1%
0.775 13
 
< 0.1%
0.77 12
 
< 0.1%
0.765 17
 
< 0.1%
0.76 14
 
< 0.1%
0.755 23
 
< 0.1%
0.75 81
0.1%
0.747 1
 
< 0.1%

Diameter
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40167916
Minimum0.055
Maximum0.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:39.500299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.055
5-th percentile0.21
Q10.345
median0.425
Q30.47
95-th percentile0.535
Maximum0.65
Range0.595
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.098026319
Coefficient of variation (CV)0.24404134
Kurtosis0.00064626408
Mean0.40167916
Median Absolute Deviation (MAD)0.06
Skewness-0.69523597
Sum36398.157
Variance0.0096091593
MonotonicityNot monotonic
2025-05-18T15:13:39.572531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45 4182
 
4.6%
0.475 3307
 
3.6%
0.455 2715
 
3.0%
0.4 2667
 
2.9%
0.47 2441
 
2.7%
0.465 2272
 
2.5%
0.46 2259
 
2.5%
0.425 2227
 
2.5%
0.44 2182
 
2.4%
0.435 2131
 
2.4%
Other values (116) 64232
70.9%
ValueCountFrequency (%)
0.055 1
 
< 0.1%
0.06 1
 
< 0.1%
0.065 1
 
< 0.1%
0.075 1
 
< 0.1%
0.085 2
 
< 0.1%
0.09 12
 
< 0.1%
0.095 4
 
< 0.1%
0.1 20
 
< 0.1%
0.103 1
 
< 0.1%
0.105 73
0.1%
ValueCountFrequency (%)
0.65 1
 
< 0.1%
0.635 2
 
< 0.1%
0.63 16
 
< 0.1%
0.625 8
 
< 0.1%
0.62 6
 
< 0.1%
0.615 5
 
< 0.1%
0.6115 1
 
< 0.1%
0.61 6
 
< 0.1%
0.605 11
 
< 0.1%
0.6 43
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13546406
Minimum0
Maximum1.13
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:39.646956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q10.11
median0.14
Q30.16
95-th percentile0.195
Maximum1.13
Range1.13
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.038007562
Coefficient of variation (CV)0.28057304
Kurtosis13.454051
Mean0.13546406
Median Absolute Deviation (MAD)0.025
Skewness0.30997506
Sum12275.075
Variance0.0014445748
MonotonicityNot monotonic
2025-05-18T15:13:39.720363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15 5742
 
6.3%
0.14 5415
 
6.0%
0.155 5230
 
5.8%
0.145 5048
 
5.6%
0.135 4980
 
5.5%
0.125 4478
 
4.9%
0.175 4174
 
4.6%
0.16 3946
 
4.4%
0.165 3772
 
4.2%
0.13 3603
 
4.0%
Other values (80) 44227
48.8%
ValueCountFrequency (%)
0 6
 
< 0.1%
0.004 1
 
< 0.1%
0.005 3
 
< 0.1%
0.009 1
 
< 0.1%
0.01 4
 
< 0.1%
0.015 16
 
< 0.1%
0.019 1
 
< 0.1%
0.02 29
 
< 0.1%
0.025 100
0.1%
0.03 129
0.1%
ValueCountFrequency (%)
1.13 2
 
< 0.1%
1 1
 
< 0.1%
0.515 1
 
< 0.1%
0.5 4
 
< 0.1%
0.35 1
 
< 0.1%
0.3 1
 
< 0.1%
0.265 2
 
< 0.1%
0.26 2
 
< 0.1%
0.255 2
 
< 0.1%
0.25 15
< 0.1%

Whole weight
Real number (ℝ)

High correlation 

Distinct3175
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78903495
Minimum0.002
Maximum2.8255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:39.789029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.1075
Q10.419
median0.7995
Q31.0675
95-th percentile1.6185
Maximum2.8255
Range2.8235
Interquartile range (IQR)0.6485

Descriptive statistics

Standard deviation0.4576707
Coefficient of variation (CV)0.58003856
Kurtosis-0.18513558
Mean0.78903495
Median Absolute Deviation (MAD)0.322
Skewness0.42931626
Sum71498.402
Variance0.20946247
MonotonicityNot monotonic
2025-05-18T15:13:39.859500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5805 485
 
0.5%
0.7665 477
 
0.5%
0.974 361
 
0.4%
0.2225 347
 
0.4%
0.8295 325
 
0.4%
0.874 318
 
0.4%
0.6355 318
 
0.4%
1.4385 310
 
0.3%
0.879 306
 
0.3%
1.1345 301
 
0.3%
Other values (3165) 87067
96.1%
ValueCountFrequency (%)
0.002 2
 
< 0.1%
0.005 2
 
< 0.1%
0.0055 2
 
< 0.1%
0.0065 1
 
< 0.1%
0.008 6
 
< 0.1%
0.0095 1
 
< 0.1%
0.0105 34
< 0.1%
0.011 4
 
< 0.1%
0.0115 2
 
< 0.1%
0.012 2
 
< 0.1%
ValueCountFrequency (%)
2.8255 3
 
< 0.1%
2.7885 1
 
< 0.1%
2.7795 4
 
< 0.1%
2.719 1
 
< 0.1%
2.657 3
 
< 0.1%
2.6195 1
 
< 0.1%
2.555 2
 
< 0.1%
2.55 3
 
< 0.1%
2.548 4
 
< 0.1%
2.526 11
< 0.1%

Whole weight.1
Real number (ℝ)

High correlation 

Distinct1799
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34077811
Minimum0.001
Maximum1.488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:39.930472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.043
Q10.1775
median0.33
Q30.463
95-th percentile0.7105
Maximum1.488
Range1.487
Interquartile range (IQR)0.2855

Descriptive statistics

Standard deviation0.20442848
Coefficient of variation (CV)0.59988736
Kurtosis0.28401194
Mean0.34077811
Median Absolute Deviation (MAD)0.1435
Skewness0.59197329
Sum30879.608
Variance0.041791003
MonotonicityNot monotonic
2025-05-18T15:13:40.004026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.096 403
 
0.4%
0.3485 390
 
0.4%
0.2945 366
 
0.4%
0.3155 365
 
0.4%
0.4935 364
 
0.4%
0.3285 358
 
0.4%
0.3345 345
 
0.4%
0.4035 345
 
0.4%
0.5265 344
 
0.4%
0.3695 343
 
0.4%
Other values (1789) 86992
96.0%
ValueCountFrequency (%)
0.001 2
 
< 0.1%
0.0015 1
 
< 0.1%
0.002 2
 
< 0.1%
0.0025 9
 
< 0.1%
0.003 2
 
< 0.1%
0.0035 9
 
< 0.1%
0.004 4
 
< 0.1%
0.0045 29
< 0.1%
0.005 44
< 0.1%
0.0055 31
< 0.1%
ValueCountFrequency (%)
1.488 2
 
< 0.1%
1.351 4
 
< 0.1%
1.3485 1
 
< 0.1%
1.2695 1
 
< 0.1%
1.2655 1
 
< 0.1%
1.254 1
 
< 0.1%
1.253 12
< 0.1%
1.2495 1
 
< 0.1%
1.2455 3
 
< 0.1%
1.2435 1
 
< 0.1%

Whole weight.2
Real number (ℝ)

High correlation 

Distinct979
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16942184
Minimum0.0005
Maximum0.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:40.075308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0005
5-th percentile0.023
Q10.0865
median0.166
Q30.2325
95-th percentile0.3555
Maximum0.76
Range0.7595
Interquartile range (IQR)0.146

Descriptive statistics

Standard deviation0.10090889
Coefficient of variation (CV)0.59560731
Kurtosis-0.20372097
Mean0.16942184
Median Absolute Deviation (MAD)0.0735
Skewness0.47673334
Sum15352.16
Variance0.010182604
MonotonicityNot monotonic
2025-05-18T15:13:40.152481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1715 799
 
0.9%
0.1725 689
 
0.8%
0.2195 611
 
0.7%
0.1625 525
 
0.6%
0.2145 501
 
0.6%
0.1815 500
 
0.6%
0.0265 494
 
0.5%
0.1825 480
 
0.5%
0.1405 474
 
0.5%
0.1905 470
 
0.5%
Other values (969) 85072
93.9%
ValueCountFrequency (%)
0.0005 17
 
< 0.1%
0.001 3
 
< 0.1%
0.0015 3
 
< 0.1%
0.002 7
 
< 0.1%
0.0025 53
 
0.1%
0.003 37
 
< 0.1%
0.0035 83
0.1%
0.004 5
 
< 0.1%
0.0045 106
0.1%
0.005 169
0.2%
ValueCountFrequency (%)
0.76 1
 
< 0.1%
0.59 10
< 0.1%
0.584 1
 
< 0.1%
0.577 1
 
< 0.1%
0.575 1
 
< 0.1%
0.5675 1
 
< 0.1%
0.5655 1
 
< 0.1%
0.564 6
< 0.1%
0.56 2
 
< 0.1%
0.5565 1
 
< 0.1%

Shell weight
Real number (ℝ)

High correlation 

Distinct1129
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22589784
Minimum0.0015
Maximum1.005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:40.230136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.031
Q10.12
median0.225
Q30.305
95-th percentile0.46
Maximum1.005
Range1.0035
Interquartile range (IQR)0.185

Descriptive statistics

Standard deviation0.13020334
Coefficient of variation (CV)0.5763815
Kurtosis0.096048966
Mean0.22589784
Median Absolute Deviation (MAD)0.0905
Skewness0.47909249
Sum20469.733
Variance0.016952909
MonotonicityNot monotonic
2025-05-18T15:13:40.304429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.24 1628
 
1.8%
0.22 1269
 
1.4%
0.25 1259
 
1.4%
0.275 1211
 
1.3%
0.265 1200
 
1.3%
0.295 1131
 
1.2%
0.27 1130
 
1.2%
0.17 1094
 
1.2%
0.26 1049
 
1.2%
0.285 1008
 
1.1%
Other values (1119) 78636
86.8%
ValueCountFrequency (%)
0.0015 4
 
< 0.1%
0.0018 1
 
< 0.1%
0.002 1
 
< 0.1%
0.0025 8
 
< 0.1%
0.003 14
 
< 0.1%
0.0035 22
 
< 0.1%
0.004 20
 
< 0.1%
0.0045 4
 
< 0.1%
0.005 299
0.3%
0.0055 11
 
< 0.1%
ValueCountFrequency (%)
1.005 1
 
< 0.1%
0.897 2
 
< 0.1%
0.89 1
 
< 0.1%
0.885 24
< 0.1%
0.858 1
 
< 0.1%
0.85 2
 
< 0.1%
0.8455 1
 
< 0.1%
0.815 3
 
< 0.1%
0.78 1
 
< 0.1%
0.7675 1
 
< 0.1%

Rings
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6967941
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.1 KiB
2025-05-18T15:13:40.549895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median9
Q311
95-th percentile16
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1762209
Coefficient of variation (CV)0.32755372
Kurtosis2.6129342
Mean9.6967941
Median Absolute Deviation (MAD)2
Skewness1.204273
Sum878675
Variance10.088379
MonotonicityNot monotonic
2025-05-18T15:13:40.609833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9 17465
19.3%
8 14499
16.0%
10 12464
13.8%
7 9008
9.9%
11 8407
9.3%
6 5411
 
6.0%
12 4719
 
5.2%
13 4074
 
4.5%
5 2862
 
3.2%
14 2507
 
2.8%
Other values (18) 9199
10.2%
ValueCountFrequency (%)
1 25
 
< 0.1%
2 29
 
< 0.1%
3 386
 
0.4%
4 1402
 
1.5%
5 2862
 
3.2%
6 5411
 
6.0%
7 9008
9.9%
8 14499
16.0%
9 17465
19.3%
10 12464
13.8%
ValueCountFrequency (%)
29 24
 
< 0.1%
27 41
 
< 0.1%
26 18
 
< 0.1%
25 22
 
< 0.1%
24 29
 
< 0.1%
23 180
 
0.2%
22 108
 
0.1%
21 255
 
0.3%
20 507
0.6%
19 639
0.7%

Interactions

2025-05-18T15:13:38.382716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:33.970490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.493119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.046357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.570158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.076804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.609371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.151304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.871994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.438881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.023373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.552649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.102335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.626619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.131736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.669081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.392695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.927103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.497777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.084101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.617514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.160513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.684562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.193493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.731051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.453270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.985191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.555569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.139437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.678487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.217406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.740412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.255476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.790930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.512166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.040637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.611910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.198904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.740097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.273536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.794613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.315767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.849334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.571198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.097666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.668191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.255368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.801691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.330517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.848920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.372771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.908955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.630224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.153217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.728811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.316018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.865903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.392919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.907285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.434184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.970491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.692712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.212392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.788009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.377300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.927167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.451887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.966322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.493390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.031154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.753311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.270205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.846761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.436054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:34.986989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:35.511861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.021250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:36.551604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.090424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:37.812923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-18T15:13:38.326141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-18T15:13:40.660425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DiameterHeightLengthRingsSexShell weightWhole weightWhole weight.1Whole weight.2id
Diameter1.0000.9210.9850.7200.4860.9620.9780.9610.9620.004
Height0.9211.0000.9160.7570.4340.9410.9360.9010.9240.005
Length0.9850.9161.0000.7080.4800.9560.9760.9640.9610.005
Rings0.7200.7570.7081.0000.4100.7870.7360.6620.7240.003
Sex0.4860.4340.4800.4101.0000.4960.4960.4700.4960.008
Shell weight0.9620.9410.9560.7870.4961.0000.9740.9340.9550.005
Whole weight0.9780.9360.9760.7360.4960.9741.0000.9770.9800.005
Whole weight.10.9610.9010.9640.6620.4700.9340.9771.0000.9590.003
Whole weight.20.9620.9240.9610.7240.4960.9550.9800.9591.0000.004
id0.0040.0050.0050.0030.0080.0050.0050.0030.0041.000

Missing values

2025-05-18T15:13:38.929172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-18T15:13:39.007766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idSexLengthDiameterHeightWhole weightWhole weight.1Whole weight.2Shell weightRings
00F0.5500.4300.1500.77150.32850.14650.240011
11F0.6300.4900.1451.13000.45800.27650.320011
22I0.1600.1100.0250.02100.00550.00300.00506
33M0.5950.4750.1500.91450.37550.20550.250010
44I0.5550.4250.1300.78200.36950.16000.19759
55F0.6100.4800.1701.20100.53350.31350.308510
66M0.4150.3250.1100.33150.16550.07150.13009
77F0.6100.4900.1501.11650.49550.29450.29509
88I0.2050.1500.0400.04600.01450.01050.01004
99I0.5650.4250.1250.65100.37950.14200.18008
idSexLengthDiameterHeightWhole weightWhole weight.1Whole weight.2Shell weightRings
9060590605M0.5600.4500.1550.90550.39250.17750.28009
9060690606M0.5750.4500.1651.09850.37650.21500.400014
9060790607F0.5550.4250.1550.87900.34100.20650.250010
9060890608I0.3500.2650.0750.17350.07600.05900.05256
9060990609F0.6500.5250.1851.70700.66050.35450.473514
9061090610M0.3350.2350.0750.15850.06850.03700.04506
9061190611M0.5550.4250.1500.87900.38650.18150.24009
9061290612I0.4350.3300.0950.32150.15100.07850.08156
9061390613I0.3450.2700.0750.20000.09800.04900.07006
9061490614I0.4250.3250.1000.34550.15250.07850.10508